An Efficient Image Regeneration Framework for Metal Artifact Impacts

Abstract

Image reconstruction is required in most of the medical analysis procedures to cross co-ordinate various diagnostic steps. Filtered back propagation is commonly used for image reconstruction techniques in medical screening systems such as x-ray computer tomography, which produces high-impact addresses in many cases. The presence of hard materials such as metals can directly attenuate the complete x-ray signals and create artifacts during the back propagation reconstruction technique. The metal artifacts need to be identified unrestricted during the Diagnostic procedures. The proposed system is focused on creating a robot architecture that detects the reflections happening in the screening images and enhances the image quality by removing the artifact reflections. The problem of artifact generation through metal are thoroughly analysed and removed to provide a high-quality imaging system. The proposed system considered an independent component analysis technique to remove the reflected pixel intensity interrupting image quality. The results of the system are evaluated by measuring the Power signal to noise ratio (PSNR), Mean square error (MSE), and structural similarity index (SSIM). The proposed system is compared with the existing state of art approaches regarding performance statistics.